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Prof. Gang Chen
University of North Carolina at Charlotte

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0 High Resolution
0 Remote Sensing
0 GEOBIA
0 Land Cover Land Use Change
0 forest disturbances

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GEOBIA
forest disturbances

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Journal article
Published: 30 May 2021 in Sustainability
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The Thai government’s project called “Eastern Economic Corridor (EEC)” was announced in 2016 to stimulate economic development and help the country escape from the middle-income trap. The project provides investment incentives for the private sector and the infrastructure development of land, rail, water, and air transportation. The EEC project encompasses three provinces in the eastern region of Thailand because of their strategic locations near deep seaports and natural resources in the Gulf of Thailand. Clearly, this policy will lead to dramatic changes in land uses and the livelihoods of the people in these three provinces. However, the extent to which land use changes will occur because of this project remains unclear. This study aims to analyze land use changes in the eastern region of Thailand using a Cellular Automata–Markov model. The results show that land uses of the coastal areas have become more urbanized than inland areas, which are primarily agricultural lands. The predicted land uses suggest shrinking agricultural lands of paddy fields, field crops, and horticulture lands but expanding perennial lands. These changes in land uses highlight challenges in urban administration and management as well as threats to Thailand’s agricultural cultures in the future.

ACS Style

Nij Tontisirin; Sutee Anantsuksomsri. Economic Development Policies and Land Use Changes in Thailand: From the Eastern Seaboard to the Eastern Economic Corridor. Sustainability 2021, 13, 6153 .

AMA Style

Nij Tontisirin, Sutee Anantsuksomsri. Economic Development Policies and Land Use Changes in Thailand: From the Eastern Seaboard to the Eastern Economic Corridor. Sustainability. 2021; 13 (11):6153.

Chicago/Turabian Style

Nij Tontisirin; Sutee Anantsuksomsri. 2021. "Economic Development Policies and Land Use Changes in Thailand: From the Eastern Seaboard to the Eastern Economic Corridor." Sustainability 13, no. 11: 6153.

Original paper
Published: 03 March 2021 in The Annals of Regional Science
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Infrastructure investments have long been a key factor driving the economic and urban development of a country. These investments usually require a large amount of funds, but funding for such investments is often limited to catching up with the growing urban population, especially for cities in emerging economies. As a result, finding alternative funding for infrastructure investment is increasingly important. Land value capture (LVC) is one of the mechanisms that can be used to fund infrastructure investment. An important policy question for the land value capture is determining how much of the increment land values are due to infrastructure investment. This research aims to assess the land value increment due to proximity to public transit, using Bangkok as a case study. The analysis employs hedonic regressions of low-rise residential real estate projects from 2009 to 2017 in the Bangkok Metropolitan Region. The results suggest that the proximity to the nearest mass transit stations has driven up the land values of residential lands in real estate development projects. In addition, the bid-rent coefficients over time have increased in magnitude in the latter years as more transit stations have opened, suggesting the presence of temporal effects from various stages of mass transit investments on land values. The analysis also assesses the value that can be captured through a tax-based LVC mechanism around a new mass transit station.

ACS Style

Nij Tontisirin; Sutee Anantsuksomsri. Measuring transit accessibility benefits and their implications on land value capture: a case study of the Bangkok Metropolitan Region. The Annals of Regional Science 2021, 67, 415 -449.

AMA Style

Nij Tontisirin, Sutee Anantsuksomsri. Measuring transit accessibility benefits and their implications on land value capture: a case study of the Bangkok Metropolitan Region. The Annals of Regional Science. 2021; 67 (2):415-449.

Chicago/Turabian Style

Nij Tontisirin; Sutee Anantsuksomsri. 2021. "Measuring transit accessibility benefits and their implications on land value capture: a case study of the Bangkok Metropolitan Region." The Annals of Regional Science 67, no. 2: 415-449.

Journal article
Published: 07 February 2021 in Forest Ecology and Management
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Forest ecosystems are increasingly affected by a range of tree mortality events, which may permanently alter forest functional traits and disrupt their ecosystem services. While individual forest disturbances are well studied, interactions between multiple disturbances and changes of spatial patterns of forested landscapes are rarely quantified. In this study, we aim to analyze the role of wildfire in the Big Sur ecoregion of California on the spread of Phytophthora ramorum, an invasive pathogen which causes sudden oak death, the most important driver of mortality across 1000 km of coastal, fire-prone mixed conifer, evergreen hardwood, and woodlands. We investigated two questions specific to the impacts of these disturbances at the landscape scale: (i) did rates of P. ramorum caused tree mortality change after wildfire? (ii) Following the wildfire, to what degree did the continued disease-driven mortality alter forest distribution? To answer these questions, we analyzed remote-sensing-derived products of post-fire burn severity and maps of disease-driven tree mortality. Quantification of burn severity and post fire disease mortality for the burned and unburned areas provided reference conditions for statistical hypothesis tests. The results from statistical and three landscape pattern analyses (area, shape, and isolation) suggest a significant role of wildfire in the reemergence of this invasive pathogen. First, rates of disease caused mortality after wildfire was negatively associated with burn severity suggesting some fire-driven containment of disease during post-fire forest recovery. Second, disease was positively correlated with the distance to fire boundary in unburned areas suggesting the effects of fire on disease extended into unburned areas while attenuating with distance from the burn. Lastly, wildfire reduced area, edge density and isolation of healthy tree patches and these effects did not recover to pre-fire levels for any of the three metrics after eight years of vegetation recovery. Given the widespread prevalence of disease-driven mortality, the importance and frequency of fire, as well as the naturalization of Phytophthora ramorum across a broad geographic area, these fire-disease interactions have potential to shape forest structure and disease dynamics across millions of acres of forested wildlands in California and Oregon.

ACS Style

Yinan He; Gang Chen; Richard C. Cobb; Kaiguang Zhao; Ross K. Meentemeyer. Forest landscape patterns shaped by interactions between wildfire and sudden oak death disease. Forest Ecology and Management 2021, 486, 118987 .

AMA Style

Yinan He, Gang Chen, Richard C. Cobb, Kaiguang Zhao, Ross K. Meentemeyer. Forest landscape patterns shaped by interactions between wildfire and sudden oak death disease. Forest Ecology and Management. 2021; 486 ():118987.

Chicago/Turabian Style

Yinan He; Gang Chen; Richard C. Cobb; Kaiguang Zhao; Ross K. Meentemeyer. 2021. "Forest landscape patterns shaped by interactions between wildfire and sudden oak death disease." Forest Ecology and Management 486, no. : 118987.

Letter
Published: 24 October 2020 in Remote Sensing
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Invasive plants are a major agent threatening biodiversity conservation and directly affecting our living environment. This study aims to evaluate the potential of deep learning, one of the fastest-growing trends in machine learning, to detect plant invasion in urban parks using high-resolution (0.1 m) aerial image time series. Capitalizing on a state-of-the-art, popular architecture residual neural network (ResNet), we examined key challenges applying deep learning to detect plant invasion: relatively limited training sample size (invasion often confirmed in the field) and high forest contextual variation in space (from one invaded park to another) and over time (caused by varying stages of invasion and the difference in illumination condition). To do so, our evaluations focused on a widespread exotic plant, autumn olive (Elaeagnus umbellate), that has invaded 20 urban parks across Mecklenburg County (1410 km2) in North Carolina, USA. The results demonstrate a promising spatial and temporal generalization capacity of deep learning to detect urban invasive plants. In particular, the performance of ResNet was consistently over 96.2% using training samples from 8 (out of 20) or more parks. The model trained by samples from only four parks still achieved an accuracy of 77.4%. ResNet was further found tolerant of high contextual variation caused by autumn olive’s progressive invasion and the difference in illumination condition over the years. Our findings shed light on prioritized mitigation actions for effectively managing urban invasive plants.

ACS Style

Dipanwita Dutta; Gang Chen; Chen Chen; Sara Gagné; Changlin Li; Christa Rogers; Christopher Matthews. Detecting Plant Invasion in Urban Parks with Aerial Image Time Series and Residual Neural Network. Remote Sensing 2020, 12, 3493 .

AMA Style

Dipanwita Dutta, Gang Chen, Chen Chen, Sara Gagné, Changlin Li, Christa Rogers, Christopher Matthews. Detecting Plant Invasion in Urban Parks with Aerial Image Time Series and Residual Neural Network. Remote Sensing. 2020; 12 (21):3493.

Chicago/Turabian Style

Dipanwita Dutta; Gang Chen; Chen Chen; Sara Gagné; Changlin Li; Christa Rogers; Christopher Matthews. 2020. "Detecting Plant Invasion in Urban Parks with Aerial Image Time Series and Residual Neural Network." Remote Sensing 12, no. 21: 3493.

Journal article
Published: 22 September 2020 in Remote Sensing
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Although vegetation phenology thresholds have been developed for a wide range of mapping applications, their use for assessing the distribution of natural bamboo and the related carbon stocks is still limited, especially in Southeast Asia. Here, we used Google Earth Engine (GEE) to collect time-series of Landsat 8 Operational Land Imager (OLI) and Sentinel-2 images and employed a phenology-based threshold classification method (PBTC) to map the natural bamboo distribution and estimate carbon stocks in Siem Reap Province, Cambodia. We processed 337 collections of Landsat 8 OLI for phenological assessment and generated 121 phenological profiles of the average vegetation index for three vegetation land cover categories from 2015 to 2018. After determining the minimum and maximum threshold values for bamboo during the leaf-shedding phenology stage, the PBTC method was applied to produce a seasonal composite enhanced vegetation index (EVI) for Landsat collections and assess the bamboo distributions in 2015 and 2018. Bamboo distributions in 2019 were then mapped by applying the EVI phenological threshold values for 10 m resolution Sentinel-2 satellite imagery by accessing 442 tiles. The overall Landsat 8 OLI bamboo maps for 2015 and 2018 had user’s accuracies (UAs) of 86.6% and 87.9% and producer’s accuracies (PAs) of 95.7% and 97.8%, respectively, and a UA of 86.5% and PA of 91.7% were obtained from Sentinel-2 imagery for 2019. Accordingly, carbon stocks of natural bamboo by district in Siem Reap at the province level were estimated. Emission reductions from the protection of natural bamboo can be used to offset 6% of the carbon emissions from tourists who visit this tourism-destination province. It is concluded that a combination of GEE and PBTC and the increasing availability of remote sensing data make it possible to map the natural distribution of bamboo and carbon stocks.

ACS Style

Manjunatha Venkatappa; Sutee Anantsuksomsri; Jose Castillo; Benjamin Smith; Nophea Sasaki. Mapping the Natural Distribution of Bamboo and Related Carbon Stocks in the Tropics Using Google Earth Engine, Phenological Behavior, Landsat 8, and Sentinel-2. Remote Sensing 2020, 12, 3109 .

AMA Style

Manjunatha Venkatappa, Sutee Anantsuksomsri, Jose Castillo, Benjamin Smith, Nophea Sasaki. Mapping the Natural Distribution of Bamboo and Related Carbon Stocks in the Tropics Using Google Earth Engine, Phenological Behavior, Landsat 8, and Sentinel-2. Remote Sensing. 2020; 12 (18):3109.

Chicago/Turabian Style

Manjunatha Venkatappa; Sutee Anantsuksomsri; Jose Castillo; Benjamin Smith; Nophea Sasaki. 2020. "Mapping the Natural Distribution of Bamboo and Related Carbon Stocks in the Tropics Using Google Earth Engine, Phenological Behavior, Landsat 8, and Sentinel-2." Remote Sensing 12, no. 18: 3109.

Journal article
Published: 22 September 2020 in Remote Sensing
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Digital and scalable technologies are increasingly important for rapid and large-scale assessment and monitoring of land cover change. Until recently, little research has existed on how these technologies can be specifically applied to the monitoring of Reducing Emissions from Deforestation and Forest Degradation (REDD+) activities. Using the Google Earth Engine (GEE) cloud computing platform, we applied the recently developed phenology-based threshold classification method (PBTC) for detecting and mapping forest cover and carbon stock changes in Siem Reap province, Cambodia, between 1990 and 2018. The obtained PBTC maps were validated using Google Earth high resolution historical imagery and reference land cover maps by creating 3771 systematic 5 × 5 km spatial accuracy points. The overall cumulative accuracy of this study was 92.1% and its cumulative Kappa was 0.9, which are sufficiently high to apply the PBTC method to detect forest land cover change. Accordingly, we estimated the carbon stock changes over a 28-year period in accordance with the Good Practice Guidelines of the Intergovernmental Panel on Climate Change. We found that 322,694 ha of forest cover was lost in Siem Reap, representing an annual deforestation rate of 1.3% between 1990 and 2018. This loss of forest cover was responsible for carbon emissions of 143,729,440 MgCO2 over the same period. If REDD+ activities are implemented during the implementation period of the Paris Climate Agreement between 2020 and 2030, about 8,256,746 MgCO2 of carbon emissions could be reduced, equivalent to about USD 6-115million annually depending on chosen carbon prices. Our case study demonstrates that the GEE and PBTC method can be used to detect and monitor forest cover change and carbon stock changes in the tropics with high accuracy.

ACS Style

Manjunatha Venkatappa; Nophea Sasaki; Sutee Anantsuksomsri; Benjamin Smith. Applications of the Google Earth Engine and Phenology-Based Threshold Classification Method for Mapping Forest Cover and Carbon Stock Changes in Siem Reap Province, Cambodia. Remote Sensing 2020, 12, 3110 .

AMA Style

Manjunatha Venkatappa, Nophea Sasaki, Sutee Anantsuksomsri, Benjamin Smith. Applications of the Google Earth Engine and Phenology-Based Threshold Classification Method for Mapping Forest Cover and Carbon Stock Changes in Siem Reap Province, Cambodia. Remote Sensing. 2020; 12 (18):3110.

Chicago/Turabian Style

Manjunatha Venkatappa; Nophea Sasaki; Sutee Anantsuksomsri; Benjamin Smith. 2020. "Applications of the Google Earth Engine and Phenology-Based Threshold Classification Method for Mapping Forest Cover and Carbon Stock Changes in Siem Reap Province, Cambodia." Remote Sensing 12, no. 18: 3110.

Journal article
Published: 29 August 2020 in Remote Sensing
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The information of building types is highly needed for urban planning and management, especially in high resolution building modeling in which buildings are the basic spatial unit. However, in many parts of the world, this information is still missing. In this paper, we proposed a framework to derive the information of building type using geospatial data, including point-of-interest (POI) data, building footprints, land use polygons, and roads, from Gaode and Baidu Maps. First, we used natural language processing (NLP)-based approaches (i.e., text similarity measurement and topic modeling) to automatically reclassify POI categories into which can be used to directly infer building types. Second, based on the relationship between building footprints and POIs, we identified building types using two indicators of type ratio and area ratio. The proposed framework was tested using over 440,000 building footprints in Beijing, China. Our NLP-based approaches and building type identification methods show overall accuracies of 89.0% and 78.2%, and kappa coefficient of 0.71 and 0.83, respectively. The proposed framework is transferrable to other China cities for deriving the information of building types from web mapping platforms. The data products generated from this study are of great use for quantitative urban studies at the building level.

ACS Style

Wei Chen; Yuyu Zhou; Qiusheng Wu; Gang Chen; Xin Huang; Bailang Yu. Urban Building Type Mapping Using Geospatial Data: A Case Study of Beijing, China. Remote Sensing 2020, 12, 2805 .

AMA Style

Wei Chen, Yuyu Zhou, Qiusheng Wu, Gang Chen, Xin Huang, Bailang Yu. Urban Building Type Mapping Using Geospatial Data: A Case Study of Beijing, China. Remote Sensing. 2020; 12 (17):2805.

Chicago/Turabian Style

Wei Chen; Yuyu Zhou; Qiusheng Wu; Gang Chen; Xin Huang; Bailang Yu. 2020. "Urban Building Type Mapping Using Geospatial Data: A Case Study of Beijing, China." Remote Sensing 12, no. 17: 2805.

Journal article
Published: 01 August 2020 in Journal of Disaster Research
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This paper aims to identify the root causes that exacerbated the economic damage from the 2011 Chao Phraya river flood disaster in central Thailand industrial complex area. Finding root causes is crucial for learning from disasters; however, there has not been much investigation of the economic damage root causes with regard to the 2011 Chao Phraya river flood disaster. This paper seeks to investigate the root causes of the economic damage by organizing the existing analytical frameworks, tools and approaches to clarify why industrial parks and estates experienced such substantial economic devastation that resonated worldwide. The study’s research design includes a social background survey, in-depth interview surveys and an investigation of the disaster’s root causes. Through the research, inadequate urban and land use planning facilitated by a decentralization policy, foreign companies settlement in the country, which involved urbanization and relocation without proper risk assessment, information, and knowledge, and supplier’s responsibility based on the supply chain’s structure, are detected as root causes for the high economic damage in the industrial complex area. This study also provides key lessons essential to building regional resilience in industrial complex areas: 1) considering the potential risks of regional planning, which include both socio-economic and climate changes; 2) clarifying the roles of companies, regions, and nations in sharing risk information with related stakeholders before, during, and after a disaster; and 3) building horizontal and vertical collaborations among all related stakeholders.

ACS Style

Tadashi Nakasu; Mamoru Miyamoto; Ruttiya Bhula-Or; Tartat Mokkhamakkul; Sutee Anantsuksomsri; Yot Amornkitvikai; Sutpratana Duangkaew; Toshio Okazumi; Tokyo Policy Secretary To The Member Of House Of Councilor. Finding the Devastating Economic Disaster’s Root Causes of the 2011 Flood in Thailand: Why Did Supply Chains Make the Disaster Worse? Journal of Disaster Research 2020, 15, 556 -570.

AMA Style

Tadashi Nakasu, Mamoru Miyamoto, Ruttiya Bhula-Or, Tartat Mokkhamakkul, Sutee Anantsuksomsri, Yot Amornkitvikai, Sutpratana Duangkaew, Toshio Okazumi, Tokyo Policy Secretary To The Member Of House Of Councilor. Finding the Devastating Economic Disaster’s Root Causes of the 2011 Flood in Thailand: Why Did Supply Chains Make the Disaster Worse? Journal of Disaster Research. 2020; 15 (5):556-570.

Chicago/Turabian Style

Tadashi Nakasu; Mamoru Miyamoto; Ruttiya Bhula-Or; Tartat Mokkhamakkul; Sutee Anantsuksomsri; Yot Amornkitvikai; Sutpratana Duangkaew; Toshio Okazumi; Tokyo Policy Secretary To The Member Of House Of Councilor. 2020. "Finding the Devastating Economic Disaster’s Root Causes of the 2011 Flood in Thailand: Why Did Supply Chains Make the Disaster Worse?" Journal of Disaster Research 15, no. 5: 556-570.

Journal article
Published: 01 August 2020 in Journal of Disaster Research
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Many cities and regions have recently experienced economic and environmental losses due to natural disasters. Economic losses are particularly high in urban areas where population and many economic activities are highly concentrated. Urban communities’ abilities and capacities to cope with natural disasters are essential to understand the impacts of natural disasters. Urban communities’ coping capacity is found to be closely linked to social capital of such communities. This paper aims to assess the natural disaster coping capacity of urban residents with social capital approach. The case study is Bangkok, Thailand. Using principal component analysis (PCA), the analysis shows that social cohesion, empowerment, and trust plays a key role in social capital level of Bangkok residents. Mapping social capital index at the district level suggests that urbanization may be contributable to the level of social capital.

ACS Style

Sutee Anantsuksomsri; Nij Tontisirin. Assessment of Natural Disaster Coping Capacity from Social Capital Perspectives: A Case Study of Bangkok. Journal of Disaster Research 2020, 15, 571 -578.

AMA Style

Sutee Anantsuksomsri, Nij Tontisirin. Assessment of Natural Disaster Coping Capacity from Social Capital Perspectives: A Case Study of Bangkok. Journal of Disaster Research. 2020; 15 (5):571-578.

Chicago/Turabian Style

Sutee Anantsuksomsri; Nij Tontisirin. 2020. "Assessment of Natural Disaster Coping Capacity from Social Capital Perspectives: A Case Study of Bangkok." Journal of Disaster Research 15, no. 5: 571-578.

Journal article
Published: 01 August 2020 in Journal of Disaster Research
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This study aims at clarifying households’ responses to the flood in Thailand. The result of this study helps fill the gap in literature about the factor affecting a household’s decision to evacuate in response to the flood, as such a decision varies with the type of natural disaster. The result of the study confirms that more vulnerable people are less likely to evacuate. However, they are more likely to evacuate, if at least one of their household members has reduced mobility. People in flood-prone areas exhibited moral hazards. Furthermore, people with relatively secured employment statuses are more likely to stay in the flood-prone area, to minimize their losses from the flood. If households with management-level employees received real-time and accurate updates about the flood, the decision to evacuate would be freely decided by such households, which can minimize their losses. Similarly, real-time and accurate data about potential damages and losses can reduce moral hazards. Thus, it is necessary for national and local governments to understand area-specific characteristics of people and linkages between societal vulnerability and economic resilience. The study’s implications highlight the importance of developing disaster management strategies in an integrated area-based approach.

ACS Style

Ruttiya Bhula-Or; Tadashi Nakasu; Tartat Mokkhamakkul; Sutee Anantsuksomsri; Yot Amornkitvikai; Kullachart Prathumchai; Sutpratana Duangkaew. Households’ Evacuation Decisions in Response to the 2011 Flood in Thailand. Journal of Disaster Research 2020, 15, 599 -608.

AMA Style

Ruttiya Bhula-Or, Tadashi Nakasu, Tartat Mokkhamakkul, Sutee Anantsuksomsri, Yot Amornkitvikai, Kullachart Prathumchai, Sutpratana Duangkaew. Households’ Evacuation Decisions in Response to the 2011 Flood in Thailand. Journal of Disaster Research. 2020; 15 (5):599-608.

Chicago/Turabian Style

Ruttiya Bhula-Or; Tadashi Nakasu; Tartat Mokkhamakkul; Sutee Anantsuksomsri; Yot Amornkitvikai; Kullachart Prathumchai; Sutpratana Duangkaew. 2020. "Households’ Evacuation Decisions in Response to the 2011 Flood in Thailand." Journal of Disaster Research 15, no. 5: 599-608.

Journal article
Published: 16 June 2020 in Remote Sensing of Environment
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Shadows are prevalent in urban environments, introducing high uncertainties to fine-scale urban land-cover mapping. In this study, we developed a Recurrent Shadow Attention Model (RSAM), capitalizing on state-of-the-art deep learning architectures, to retrieve fine-scale land-cover classes within cast and self shadows along the urban-rural gradient. The RSAM differs from the other existing shadow removal models by progressively refining the shadow detection result with two attention-based interacting modules – Shadow Detection Module (SDM) and Shadow Classification Module (SCM). To facilitate model training and validation, we also created a Shadow Semantic Annotation Database (SSAD) using the 1 m resolution (National Agriculture Imagery Program) NAIP aerial imagery. The SSAD comprises 103 image patches (500 × 500 pixels each) containing various types of shadows and six major land-cover classes – building, tree, grass/shrub, road, water, and farmland. Our results show an overall accuracy of 90.6% and Kappa of 0.82 for RSAM to extract the six land-cover classes within shadows. The model performance was stable along the urban-rural gradient, although it was slightly better in rural areas than in urban centers or suburban neighborhoods. Findings suggest that RSAM is a robust solution to eliminate the effects in high-resolution mapping both from cast and self shadows that have not received equal attention in previous studies.

ACS Style

Yindan Zhang; Gang Chen; Jelena Vukomanovic; Kunwar K. Singh; Yong Liu; Samuel Holden; Ross K. Meentemeyer. Recurrent Shadow Attention Model (RSAM) for shadow removal in high-resolution urban land-cover mapping. Remote Sensing of Environment 2020, 247, 111945 .

AMA Style

Yindan Zhang, Gang Chen, Jelena Vukomanovic, Kunwar K. Singh, Yong Liu, Samuel Holden, Ross K. Meentemeyer. Recurrent Shadow Attention Model (RSAM) for shadow removal in high-resolution urban land-cover mapping. Remote Sensing of Environment. 2020; 247 ():111945.

Chicago/Turabian Style

Yindan Zhang; Gang Chen; Jelena Vukomanovic; Kunwar K. Singh; Yong Liu; Samuel Holden; Ross K. Meentemeyer. 2020. "Recurrent Shadow Attention Model (RSAM) for shadow removal in high-resolution urban land-cover mapping." Remote Sensing of Environment 247, no. : 111945.

Journal article
Published: 06 March 2020 in Ecological Indicators
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Forest canopy cover and carbon density are two pivotal biophysical parameters for assessing urban forest structure and its ecosystem services. While canopy cover (horizontal structure) has been extensively studied for understanding the relationship between socio-ecological dynamics and urban forests, carbon density (vertical structure) received little attention in the urban setting. The goal of this study was twofold: (i) exploring the differences between canopy cover and carbon density, and their relationships with socio-ecological factors across an urbanizing landscape, and (ii) assessing the effect of neighborhood category (i.e., low, medium and high development intensity) on the relationships at the neighborhood level. We used Mecklenburg County located in the Charlotte Metropolitan area of North Carolina, United States as a case study area, where rapid urban sprawl has fragmented the pine-oak-hickory dominated forests into a range of low to high housing density neighborhoods. We observed two major findings. First, canopy cover and carbon density demonstrated a generally weak correlation across various types of residential neighborhoods, although such relationship became relatively stronger in areas featuring a higher level of development intensity. Second, ecological factors (e.g., landscape spatial patterns) were found to dominate the statistical models explaining the variance in both canopy cover and carbon density compared to urban socioeconomic factors (e.g., income and age). However, the models and the explanatory factors were different for the two forest parameters, and they varied across neighborhoods of diverse development intensities. Based upon these findings, we argue that canopy cover and carbon density are different proxy indicators of forest functioning in the urban setting, and should be independently treated in urban forest management. The best management practices should be developed at the inner-city, neighborhood level, rather than the typical city level, owing to the significant, variable influence of socio-ecological conditions across neighborhood types.

ACS Style

Gang Chen; Kunwar Singh; Jaime Lopez; Yuyu Zhou. Tree canopy cover and carbon density are different proxy indicators for assessing the relationship between forest structure and urban socio-ecological conditions. Ecological Indicators 2020, 113, 106279 .

AMA Style

Gang Chen, Kunwar Singh, Jaime Lopez, Yuyu Zhou. Tree canopy cover and carbon density are different proxy indicators for assessing the relationship between forest structure and urban socio-ecological conditions. Ecological Indicators. 2020; 113 ():106279.

Chicago/Turabian Style

Gang Chen; Kunwar Singh; Jaime Lopez; Yuyu Zhou. 2020. "Tree canopy cover and carbon density are different proxy indicators for assessing the relationship between forest structure and urban socio-ecological conditions." Ecological Indicators 113, no. : 106279.

Case report
Published: 09 July 2019 in Agriculture
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Sustainable agricultural practices necessitate accurate baseline data of crop types and their detailed spatial distribution. Compared with field surveys, remote sensing has demonstrated superior performance, offering spatially explicit crop distribution in a timely manner. Recent studies have taken advantage of remote sensing time series to capture the variation in plant phenology, inferring major crop types. However, such an approach was rarely used to extract detailed, multiple crop types spanning a large area, and the impact of topography has yet to be well analyzed in mountainous regions. This study aims to answer two questions in crop type extraction: (i) Is it feasible to accurately map multiple crop types over a large mountainous area with phenology-based modeling? (ii) What are the effects of topography in such modeling? To answer the questions, phenological metrics were extracted from MODIS (Moderate Resolution Imaging Spectroradiometer) satellite time series, and the random forests classifier was used to map 12 crop types in South China (236,700 km2), featuring a subtropical monsoon climate and high topographic variation. Our study revealed promising results using MODIS EVI (Enhanced Vegetation Index) and NDVI (Normalized Difference Vegetation Index) time series, although EVI outperformed NDVI (overall accuracy: 85% versus 81%). The spectral and temporal metrics of plant phenology significantly contributed to crop identification, where the spectral information exhibited greater importance. The increase of slope led to a decrease in model accuracy in general. However, uniformly distributed tree plantations (e.g., tea-oil camellia, gum, and tea trees) being cultivated on large slopes (>15 degrees) achieved accuracies greater than 80%.

ACS Style

Yanfei Wei; Xinhua Tong; Gang Chen; Deqiang Liu; Zhenfeng Han. Remote Detection of Large-Area Crop Types: The Role of Plant Phenology and Topography. Agriculture 2019, 9, 150 .

AMA Style

Yanfei Wei, Xinhua Tong, Gang Chen, Deqiang Liu, Zhenfeng Han. Remote Detection of Large-Area Crop Types: The Role of Plant Phenology and Topography. Agriculture. 2019; 9 (7):150.

Chicago/Turabian Style

Yanfei Wei; Xinhua Tong; Gang Chen; Deqiang Liu; Zhenfeng Han. 2019. "Remote Detection of Large-Area Crop Types: The Role of Plant Phenology and Topography." Agriculture 9, no. 7: 150.

Journal article
Published: 12 June 2019 in Remote Sensing of Environment
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Forest ecosystems have been increasingly affected by a variety of disturbances, including emerging infectious diseases (EIDs), causing extensive tree mortality in the Western United States. Especially over the past decade, EID outbreaks occurred more frequently and severely in forest landscapes, which have killed large numbers of trees. While tree mortality is observable from remote sensing, its symptom may be associated with both disease and non-disease disturbances (e.g., wildfire and drought). Species distribution modeling is widely used to understand species spatial preferences for certain habitat conditions, which may constrain uncertain remote sensing approaches due to limited spatial and spectral resolution. In this study, we integrated multi-sensor remote sensing and species distribution modeling to map disease-caused tree mortality in a forested area of 80,000 ha from 2005 to 2016. We selected sudden oak death (caused by pathogen P. ramorum) as a case study of a rapidly spreading emerging infectious disease, which has killed millions of oak (Quercus spp.) and tanoak (Lithocarpus densiflorus) in California over the past decades. To balance the needs for fine-scale monitoring of disease distribution patterns and satisfactory coverage at broad scales, our method applied spectral unmixing to extract sub-pixel disease presence using yearly Landsat time series. The results were improved by employing the probability of disease infection generated from a species distribution model. We calibrated and validated the method with image samples from high-spatial resolution NAIP (National Agriculture Imagery Program), and hyperspectral AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) sensors, Google Earth® imagery, and field observations. The findings reveal an annual sudden oak death infection rate of 7% from 2005 to 2016, with overall mapping accuracies ranging from 76% to 83%. The integration of multi-sensor remote sensing and species distribution modeling considerably reduced the overestimation of disease effects as compared to the use of remote sensing alone, leading to an average of 26% decrease in detecting disease-affected trees. Such integration strategy proved the effectiveness of mapping long-term, disease-caused tree mortality in forest landscapes that have experienced multiple disturbances.

ACS Style

Yinan He; Gang Chen; Christopher Potter; Ross Meentemeyer. Integrating multi-sensor remote sensing and species distribution modeling to map the spread of emerging forest disease and tree mortality. Remote Sensing of Environment 2019, 231, 111238 .

AMA Style

Yinan He, Gang Chen, Christopher Potter, Ross Meentemeyer. Integrating multi-sensor remote sensing and species distribution modeling to map the spread of emerging forest disease and tree mortality. Remote Sensing of Environment. 2019; 231 ():111238.

Chicago/Turabian Style

Yinan He; Gang Chen; Christopher Potter; Ross Meentemeyer. 2019. "Integrating multi-sensor remote sensing and species distribution modeling to map the spread of emerging forest disease and tree mortality." Remote Sensing of Environment 231, no. : 111238.

Articles
Published: 01 January 2019 in European Journal of Remote Sensing
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There exist different approaches for segmenting Very High Spatial Resolution (VHSR) remote sensing imagery with competitive performance, including object-based (e.g. Multiresolution), gradient-based (e.g. Watershed), and clustering-based (e.g. k-means) segmentation. However, they have a strong dependence on human assistance for tuning the required parameters (e.g. scale value, clusters number or tolerance thresholds), usually following a trial-and-error methodology that becomes tedious, hardly reproducible or transferable to other images, affecting negatively the methods’ robustness and efficiency. In this communication, we propose a novel method denominated Line-based segmentation (LBS) that automatically segments VHSR remote sensing imagery through a data-driven approach, bypassing the parameters’ definition by experts (i.e. region growing´s seeds and thresholds). The proposed algorithm offers flexibility and accuracy to segment regions with varying sizes and shapes, tested on different VHSR images, including multispectral images (WorldView-3, GeoEywe-1, Ikonos, QuickBird and SkySat), RGB aerial image (NAIP) and panchromatic image (Ikonos). The results revealed the LBS method shows a competitive performance compared against two well-known segmentation approaches, but without user intervention and generating consistent and repeatable segmentation results following an automatic fashion.

ACS Style

Jaime Lopez; John W. Branch; Gang Chen. Line-based image segmentation method: a new approach to segment VHSR remote sensing images automatically. European Journal of Remote Sensing 2019, 52, 613 -631.

AMA Style

Jaime Lopez, John W. Branch, Gang Chen. Line-based image segmentation method: a new approach to segment VHSR remote sensing images automatically. European Journal of Remote Sensing. 2019; 52 (1):613-631.

Chicago/Turabian Style

Jaime Lopez; John W. Branch; Gang Chen. 2019. "Line-based image segmentation method: a new approach to segment VHSR remote sensing images automatically." European Journal of Remote Sensing 52, no. 1: 613-631.

Journal article
Published: 16 November 2018 in Remote Sensing of Environment
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Forest ecosystems are subject to recurring fires as one of their most significant disturbances. Accurate mapping of burn severity is crucial for post-fire land management and vegetation regeneration monitoring. Remote-sensing-based monitoring of burn severity faces new challenges when forests experience both fire and non-fire disturbances, which may change the biophysical and biochemical properties of trees in similar ways. In this study, we develop a Disturbance Weighting Analysis Model (DWAM) for accurately mapping burn severity in a forest landscape that is jointly affected by wildfire and an emerging infectious disease – sudden oak death. Our approach treats burn severity in each basic mapping unit (e.g., 30 m grid from a post-fire Landsat image) as a linear combination of burn severity of trees affected (diseased) and not affected by the disease (healthy), weighted by their areal fractions in the unit. DWAM is calibrated using two types of inputs: i) look-up tables (LUTs) linking burn severity and post-fire spectra for diseased and healthy trees, derived from field observations, hyperspectral sensors [e.g., Airborne Visible InfraRed Imaging Spectrometer (AVIRIS)], and radiative transfer models; and ii) pre-fire fractional maps of diseased and healthy trees, derived by decomposing a pre-fire Landsat image using Multiple Endmember Spectral Mixture Analysis (MESMA). Considering the presence of tree disease in DWAM improved the overall map accuracy by 42%. The superior performance is consistent across all three stages of disease progression. Our approach demonstrates the potential for improved mapping of forest burn severity by reducing the confounding effects of other biotic disturbances.

ACS Style

Yinan He; Gang Chen; Angela De Santis; Dar A. Roberts; Yuyu Zhou; Ross Meentemeyer. A disturbance weighting analysis model (DWAM) for mapping wildfire burn severity in the presence of forest disease. Remote Sensing of Environment 2018, 221, 108 -121.

AMA Style

Yinan He, Gang Chen, Angela De Santis, Dar A. Roberts, Yuyu Zhou, Ross Meentemeyer. A disturbance weighting analysis model (DWAM) for mapping wildfire burn severity in the presence of forest disease. Remote Sensing of Environment. 2018; 221 ():108-121.

Chicago/Turabian Style

Yinan He; Gang Chen; Angela De Santis; Dar A. Roberts; Yuyu Zhou; Ross Meentemeyer. 2018. "A disturbance weighting analysis model (DWAM) for mapping wildfire burn severity in the presence of forest disease." Remote Sensing of Environment 221, no. : 108-121.

Journal article
Published: 05 October 2018 in International Journal of Environmental Research and Public Health
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Climate change, urbanization, and globalization have facilitated the spread of Aedes mosquitoes into regions that were previously unsuitable, causing an increased threat of arbovirus transmission on a global scale. While numerous studies have addressed the urban ecology of Ae. albopictus, few have accounted for socioeconomic factors that affect their range in urban regions. Here we introduce an original sampling design for Ae. albopictus, that uses a spatial optimization process to identify urban collection sites based on both geographic parameters as well as the gradient of socioeconomic variables present in Mecklenburg County, North Carolina, encompassing the city of Charlotte, a rapidly growing urban environment. We collected 3,645 specimens of Ae. albopictus (87% of total samples) across 12 weeks at the 90 optimized site locations and modelled the relationships between the abundance of gravid Ae. albopictus and a variety of neighborhood socioeconomic attributes as well as land cover characteristics. Our results demonstrate that the abundance of gravid Ae. albopictus is inversely related to the socioeconomic status of the neighborhood and directly related to both landscape heterogeneity as well as proportions of particular resident races/ethnicities. We present our results alongside a description of our novel sampling scheme and its usefulness as an approach to urban vector epidemiology. Additionally, we supply recommendations for future investigations into the socioeconomic determinants of vector-borne disease risk.

ACS Style

Ari Whiteman; Eric Delmelle; Tyler Rapp; Shi Chen; Gang Chen; Michael Dulin. A Novel Sampling Method to Measure Socioeconomic Drivers of Aedes Albopictus Distribution in Mecklenburg County, North Carolina. International Journal of Environmental Research and Public Health 2018, 15, 2179 .

AMA Style

Ari Whiteman, Eric Delmelle, Tyler Rapp, Shi Chen, Gang Chen, Michael Dulin. A Novel Sampling Method to Measure Socioeconomic Drivers of Aedes Albopictus Distribution in Mecklenburg County, North Carolina. International Journal of Environmental Research and Public Health. 2018; 15 (10):2179.

Chicago/Turabian Style

Ari Whiteman; Eric Delmelle; Tyler Rapp; Shi Chen; Gang Chen; Michael Dulin. 2018. "A Novel Sampling Method to Measure Socioeconomic Drivers of Aedes Albopictus Distribution in Mecklenburg County, North Carolina." International Journal of Environmental Research and Public Health 15, no. 10: 2179.

Journal article
Published: 13 July 2018 in ISPRS Journal of Photogrammetry and Remote Sensing
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Rubber (Hevea brasiliensis) plantations are a rapidly increasing source of land cover change in mainland Southeast Asia. Stand age of rubber plantations obtained at fine scales provides essential baseline data, informing the pace of industrial and smallholder agricultural activities in response to the changing global rubber markets, and local political and socioeconomic dynamics. In this study, we developed an integrated pixel- and object-based tree growth model using Landsat annual time series to estimate the age of rubber plantations in a 21,115 km2 tri-border region along the junction of China, Myanmar and Laos. We produced a rubber stand age map at 30 m resolution, with an accuracy of 87.00% for identifying rubber plantations and an average error of 1.53 years in age estimation. The integration of pixel- and object-based image analysis showed superior performance in building NDVI yearly time series that reduced spectral noises from background soil and vegetation in open-canopy, young rubber stands. The model parameters remained relatively stable during model sensitivity analysis, resulting in accurate age estimation robust to outliers. Compared to the typically weak statistical relationship between single-date spectral signatures and rubber tree age, Landsat image time series analysis coupled with tree growth modeling presents a viable alternative for fine-scale age estimation of rubber plantations.

ACS Style

Gang Chen; Jean-Claude Thill; Sutee Anantsuksomsri; Nij Tontisirin; Ran Tao. Stand age estimation of rubber (Hevea brasiliensis) plantations using an integrated pixel- and object-based tree growth model and annual Landsat time series. ISPRS Journal of Photogrammetry and Remote Sensing 2018, 144, 94 -104.

AMA Style

Gang Chen, Jean-Claude Thill, Sutee Anantsuksomsri, Nij Tontisirin, Ran Tao. Stand age estimation of rubber (Hevea brasiliensis) plantations using an integrated pixel- and object-based tree growth model and annual Landsat time series. ISPRS Journal of Photogrammetry and Remote Sensing. 2018; 144 ():94-104.

Chicago/Turabian Style

Gang Chen; Jean-Claude Thill; Sutee Anantsuksomsri; Nij Tontisirin; Ran Tao. 2018. "Stand age estimation of rubber (Hevea brasiliensis) plantations using an integrated pixel- and object-based tree growth model and annual Landsat time series." ISPRS Journal of Photogrammetry and Remote Sensing 144, no. : 94-104.

Editorial
Published: 02 February 2018 in GIScience & Remote Sensing
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ACS Style

Gang Chen; Qihao Weng. Special issue: Remote sensing of our changing landscapes with Geographic Object-based Image Analysis (GEOBIA). GIScience & Remote Sensing 2018, 55, 155 -158.

AMA Style

Gang Chen, Qihao Weng. Special issue: Remote sensing of our changing landscapes with Geographic Object-based Image Analysis (GEOBIA). GIScience & Remote Sensing. 2018; 55 (2):155-158.

Chicago/Turabian Style

Gang Chen; Qihao Weng. 2018. "Special issue: Remote sensing of our changing landscapes with Geographic Object-based Image Analysis (GEOBIA)." GIScience & Remote Sensing 55, no. 2: 155-158.

Journal article
Published: 18 January 2018 in GIScience & Remote Sensing
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ACS Style

Gang Chen; Qihao Weng; Geoffrey J. Hay; Yinan He. Geographic object-based image analysis (GEOBIA): emerging trends and future opportunities. GIScience & Remote Sensing 2018, 55, 159 -182.

AMA Style

Gang Chen, Qihao Weng, Geoffrey J. Hay, Yinan He. Geographic object-based image analysis (GEOBIA): emerging trends and future opportunities. GIScience & Remote Sensing. 2018; 55 (2):159-182.

Chicago/Turabian Style

Gang Chen; Qihao Weng; Geoffrey J. Hay; Yinan He. 2018. "Geographic object-based image analysis (GEOBIA): emerging trends and future opportunities." GIScience & Remote Sensing 55, no. 2: 159-182.